{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Stage 1:\n",
    "Perform statistical parsing/tagging on a document in JSON format\n",
    "\n",
    "INPUTS: JSON doc for the text input  \n",
    "OUTPUT: JSON format `ParsedGraf(id, sha1, graf)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\"777\", \"7b982e54fa330a6854a0ed5397d49223fdc70645\", [[1, \"Compatibility\", \"compatibility\", \"NN\", 1, 0], [0, \"of\", \"of\", \"IN\", 0, 1], [2, \"systems\", \"system\", \"NNS\", 1, 2], [0, \"of\", \"of\", \"IN\", 0, 3], [3, \"linear\", \"linear\", \"JJ\", 1, 4], [4, \"constraints\", \"constraint\", \"NNS\", 1, 5], [0, \"over\", \"over\", \"IN\", 0, 6], [0, \"the\", \"the\", \"DT\", 0, 7], [5, \"set\", \"set\", \"NN\", 1, 8], [0, \"of\", \"of\", \"IN\", 0, 9], [6, \"natural\", \"natural\", \"JJ\", 1, 10], [7, \"numbers\", \"number\", \"NNS\", 1, 11], [0, \".\", \".\", \".\", 0, 12]]]\n",
      "[\"777\", \"dfa572a4a2d2c0fd9254172d95b574b3f6067f63\", [[8, \"Criteria\", \"criteria\", \"NNP\", 1, 13], [0, \"of\", \"of\", \"IN\", 0, 14], [1, \"compatibility\", \"compatibility\", \"NN\", 1, 15], [0, \"of\", \"of\", \"IN\", 0, 16], [0, \"a\", \"a\", \"DT\", 0, 17], [2, \"system\", \"system\", \"NN\", 1, 18], [0, \"of\", \"of\", \"IN\", 0, 19], [3, \"linear\", \"linear\", \"NN\", 1, 20], [9, \"Diophantine\", \"diophantine\", \"NNP\", 1, 21], [10, \"equations\", \"equation\", \"NNS\", 1, 22], [0, \",\", \",\", \".\", 0, 23], [11, \"strict\", \"strict\", \"JJ\", 1, 24], [12, \"inequations\", \"inequation\", \"NNS\", 1, 25], [0, \",\", \",\", \".\", 0, 26], [0, \"and\", \"and\", \"CC\", 0, 27], [13, \"nonstrict\", \"nonstrict\", \"NN\", 1, 28], [12, \"inequations\", \"inequation\", \"NNS\", 1, 29], [14, \"are\", \"be\", \"VBP\", 1, 30], [15, \"considered\", \"consider\", \"VBN\", 1, 31], [0, \".\", \".\", \".\", 0, 32]]]\n",
      "[\"777\", \"cb9235d7c8b21321b88462fca3a0480e29aa8ec7\", [[16, \"Upper\", \"upper\", \"JJ\", 1, 33], [17, \"bounds\", \"bound\", \"NNS\", 1, 34], [0, \"for\", \"for\", \"IN\", 0, 35], [18, \"components\", \"component\", \"NNS\", 1, 36], [0, \"of\", \"of\", \"IN\", 0, 37], [0, \"a\", \"a\", \"DT\", 0, 38], [19, \"minimal\", \"minimal\", \"JJ\", 1, 39], [5, \"set\", \"set\", \"NN\", 1, 40], [0, \"of\", \"of\", \"IN\", 0, 41], [20, \"solutions\", \"solution\", \"NNS\", 1, 42], [0, \"and\", \"and\", \"CC\", 0, 43], [21, \"algorithms\", \"algorithm\", \"NNS\", 1, 44], [0, \"of\", \"of\", \"IN\", 0, 45], [22, \"construction\", \"construction\", \"NN\", 1, 46], [0, \"of\", \"of\", \"IN\", 0, 47], [19, \"minimal\", \"minimal\", \"JJ\", 1, 48], [23, \"generating\", \"generating\", \"NN\", 1, 49], [5, \"sets\", \"set\", \"NNS\", 1, 50], [0, \"of\", \"of\", \"IN\", 0, 51], [20, \"solutions\", \"solution\", \"NNS\", 1, 52], [0, \"for\", \"for\", \"IN\", 0, 53], [0, \"all\", \"all\", \"DT\", 0, 54], [24, \"types\", \"type\", \"NNS\", 1, 55], [0, \"of\", \"of\", \"IN\", 0, 56], [2, \"systems\", \"system\", \"NNS\", 1, 57], [14, \"are\", \"be\", \"VBP\", 1, 58], [25, \"given\", \"give\", \"VBN\", 1, 59], [0, \".\", \".\", \".\", 0, 60]]]\n",
      "[\"777\", \"64db26d2b1979694d377776d5e53c9254ee6f85e\", [[0, \"These\", \"these\", \"DT\", 0, 61], [26, \"criteria\", \"criterion\", \"NNS\", 1, 62], [0, \"and\", \"and\", \"CC\", 0, 63], [0, \"the\", \"the\", \"DT\", 0, 64], [27, \"corresponding\", \"correspond\", \"VBG\", 1, 65], [28, \"algorithms\", \"algorithms\", \"NN\", 1, 66], [0, \"for\", \"for\", \"IN\", 0, 67], [29, \"constructing\", \"construct\", \"VBG\", 1, 68], [0, \"a\", \"a\", \"DT\", 0, 69], [19, \"minimal\", \"minimal\", \"JJ\", 1, 70], [30, \"supporting\", \"support\", \"VBG\", 1, 71], [5, \"set\", \"set\", \"NN\", 1, 72], [0, \"of\", \"of\", \"IN\", 0, 73], [20, \"solutions\", \"solution\", \"NNS\", 1, 74], [0, \"can\", \"can\", \"MD\", 0, 75], [14, \"be\", \"be\", \"VB\", 1, 76], [31, \"used\", \"use\", \"VBN\", 1, 77], [0, \"in\", \"in\", \"IN\", 0, 78], [32, \"solving\", \"solve\", \"VBG\", 1, 79], [0, \"all\", \"all\", \"PDT\", 0, 80], [0, \"the\", \"the\", \"DT\", 0, 81], [15, \"considered\", \"consider\", \"VBN\", 1, 82], [24, \"types\", \"type\", \"NNS\", 1, 83], [2, \"systems\", \"system\", \"NNS\", 1, 84], [0, \"and\", \"and\", \"CC\", 0, 85], [2, \"systems\", \"system\", \"NNS\", 1, 86], [0, \"of\", \"of\", \"IN\", 0, 87], [33, \"mixed\", \"mixed\", \"JJ\", 1, 88], [24, \"types\", \"type\", \"NNS\", 1, 89], [0, \".\", \".\", \".\", 0, 90]]]\n"
     ]
    }
   ],
   "source": [
    "import pytextrank\n",
    "import sys\n",
    "\n",
    "path_stage0 = \"dat/mih.json\"\n",
    "path_stage1 = \"o1.json\"\n",
    "\n",
    "with open(path_stage1, 'w') as f:\n",
    "    for graf in pytextrank.parse_doc(pytextrank.json_iter(path_stage0)):\n",
    "        f.write(\"%s\\n\" % pytextrank.pretty_print(graf._asdict()))\n",
    "        # to view output in this notebook\n",
    "        print(pytextrank.pretty_print(graf))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Stage 2:\n",
    "Collect and normalize the key phrases from a parsed document\n",
    "\n",
    "INPUTS: `<stage1>`  \n",
    "OUTPUT: JSON format `RankedLexeme(text, rank, ids, pos)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\"types systems\", 0.12580188866089437, [24, 2], \"np\", 1]\n",
      "[\"mixed types\", 0.08891323868549979, [33, 24], \"np\", 1]\n",
      "[\"minimal set\", 0.07071383636856185, [19, 5], \"np\", 1]\n",
      "[\"systems\", 0.06290094433044718, [2], \"np\", 1]\n",
      "[\"strict inequations\", 0.05170005955659954, [11, 12], \"np\", 1]\n",
      "[\"considered\", 0.04535170212808048, [15], \"vbn\", 2]\n",
      "[\"types\", 0.044456619342749894, [24], \"nns\", 3]\n",
      "[\"natural numbers\", 0.035680974352343166, [6, 7], \"np\", 1]\n",
      "[\"set\", 0.035356918184280925, [5], \"nn\", 4]\n",
      "[\"minimal generating sets\", 0.035356918184280925, [19, 23, 5], \"np\", 1]\n",
      "[\"solutions\", 0.03516111710876194, [20], \"nns\", 3]\n",
      "[\"linear diophantine equations\", 0.031027760122128312, [3, 9, 10], \"np\", 1]\n",
      "[\"diophantine\", 0.027937472512821634, [9], \"np\", 1]\n",
      "[\"linear constraints\", 0.027937472512821634, [3, 4], \"np\", 1]\n",
      "[\"solving\", 0.027584454272763282, [32], \"vbg\", 1]\n",
      "[\"nonstrict inequations\", 0.02585002977829977, [13, 12], \"np\", 1]\n",
      "[\"inequations\", 0.02585002977829977, [12], \"nns\", 2]\n",
      "[\"numbers\", 0.017840487176171583, [7], \"nns\", 1]\n",
      "[\"given\", 0.01741836357524441, [25], \"vbn\", 1]\n",
      "[\"linear\", 0.015513880061064156, [3], \"nn\", 1]\n",
      "[\"constraints\", 0.013968736256410817, [4], \"nns\", 1]\n",
      "[\"equations\", 0.013964687396243649, [10], \"nns\", 1]\n",
      "[\"construction\", 0.013071555591451742, [22], \"nn\", 1]\n",
      "[\"generating\", 0.01236559416376835, [23], \"nn\", 1]\n",
      "[\"constructing\", 0.012003834512632964, [29], \"vbg\", 1]\n",
      "[\"supporting\", 0.011911768549549907, [30], \"vbg\", 1]\n",
      "[\"algorithms\", 0.011104588624268798, [21], \"nns\", 1]\n",
      "[\"nonstrict\", 0.010956410339326364, [13], \"nn\", 1]\n",
      "[\"upper bounds\", 0.010352566711446532, [16, 17], \"np\", 1]\n",
      "[\"components\", 0.009576138633057875, [18], \"nns\", 1]\n",
      "[\"compatibility\", 0.006720084796696289, [1], \"nn\", 2]\n",
      "[\"corresponding\", 0.006720084796696289, [27], \"vbg\", 1]\n",
      "[\"algorithms\", 0.006488535751111831, [28], \"nn\", 1]\n",
      "[\"bounds\", 0.005176283355723266, [17], \"nns\", 1]\n",
      "[\"criteria\", 0.0036324819147502438, [26], \"nns\", 1]\n",
      "[\"criteria\", 0.0036324819147502438, [8], \"nnp\", 1]\n"
     ]
    }
   ],
   "source": [
    "path_stage1 = \"o1.json\"\n",
    "path_stage2 = \"o2.json\"\n",
    "\n",
    "graph, ranks = pytextrank.text_rank(path_stage1)\n",
    "pytextrank.render_ranks(graph, ranks)\n",
    "\n",
    "with open(path_stage2, 'w') as f:\n",
    "    for rl in pytextrank.normalize_key_phrases(path_stage1, ranks):\n",
    "        f.write(\"%s\\n\" % pytextrank.pretty_print(rl._asdict()))\n",
    "        # to view output in this notebook\n",
    "        print(pytextrank.pretty_print(rl))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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F0hjo4gLqzY41PT46GrVq1QIgCc/WrVtx48YNDBs2DOfOnUOHDh3g7++PpKQkeHt7w8XF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\nKIpSiFQgVhRFUZRC9P8E8/2ywJGm0wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x197e01748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import networkx as nx\n",
    "import pylab as plt\n",
    "\n",
    "nx.draw(graph, with_labels=True) \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Stage 3:\n",
    "Calculate a significance weight for each sentence, using MinHash to approximate a Jaccard distance from key phrases determined by TextRank\n",
    "\n",
    "INPUTS: `<stage1> <stage2>`  \n",
    "OUTPUT: JSON format `SummarySent(dist, idx, text)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"dist\": 0.06495815088405221, \"idx\": 0, \"text\": \"Compatibility of systems of linear constraints over the set of natural numbers .\"}\n",
      "{\"dist\": 0.059241314822135904, \"idx\": 2, \"text\": \"Upper bounds for components of a minimal set of solutions and algorithms of construction of minimal generating sets of solutions for all types of systems are given .\"}\n",
      "{\"dist\": 0.05775806902914533, \"idx\": 3, \"text\": \"These criteria and the corresponding algorithms for constructing a minimal supporting set of solutions can be used in solving all the considered types systems and systems of mixed types .\"}\n",
      "{\"dist\": 0.05064812344999486, \"idx\": 1, \"text\": \"Criteria of compatibility of a system of linear Diophantine equations , strict inequations , and nonstrict inequations are considered .\"}\n"
     ]
    }
   ],
   "source": [
    "path_stage1 = \"o1.json\"\n",
    "path_stage2 = \"o2.json\"\n",
    "path_stage3 = \"o3.json\"\n",
    "\n",
    "kernel = pytextrank.rank_kernel(path_stage2)\n",
    "\n",
    "with open(path_stage3, 'w') as f:\n",
    "    for s in pytextrank.top_sentences(kernel, path_stage1):\n",
    "        f.write(pytextrank.pretty_print(s._asdict()))\n",
    "        f.write(\"\\n\")\n",
    "        # to view output in this notebook\n",
    "        print(pytextrank.pretty_print(s._asdict()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Stage 4:\n",
    "Summarize a document based on most significant sentences and key phrases\n",
    "\n",
    "INPUTS: `<stage2> <stage3>`  \n",
    "OUTPUT: Markdown format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**excerpts:** Compatibility of systems of linear constraints over the set of natural numbers. Criteria of compatibility of a system of linear Diophantine equations, strict inequations, and nonstrict inequations are considered. Upper bounds for components of a minimal set of solutions and algorithms of construction of minimal generating sets of solutions for all types of systems are given. These criteria and the corresponding algorithms for constructing a minimal supporting set of solutions can be used in solving all the considered types systems and systems of mixed types.\n",
      "\n",
      "**keywords:** natural numbers, systems, set, types, types systems, diophantine, linear constraints, linear diophantine equations, mixed types, solutions, strict inequations, minimal generating sets, minimal set\n"
     ]
    }
   ],
   "source": [
    "path_stage2 = \"o2.json\"\n",
    "path_stage3 = \"o3.json\"\n",
    "\n",
    "phrases = \", \".join(set([p for p in pytextrank.limit_keyphrases(path_stage2, phrase_limit=12)]))\n",
    "sent_iter = sorted(pytextrank.limit_sentences(path_stage3, word_limit=150), key=lambda x: x[1])\n",
    "s = []\n",
    "\n",
    "for sent_text, idx in sent_iter:\n",
    "    s.append(pytextrank.make_sentence(sent_text))\n",
    "\n",
    "graf_text = \" \".join(s)\n",
    "print(\"**excerpts:** %s\\n\\n**keywords:** %s\" % (graf_text, phrases,))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
